Common Data Models in Clinical Research

In clinical research, data models are essential for organizing, storing, and analyzing complex healthcare data. Effective data modeling ensures accurate data integration, facilitates efficient data analysis, and enhances data sharing across different systems and studies.

Let’s explore some of the most commonly used data models in clinical research and their applications.


1. CDISC Standards (Clinical Data Interchange Standards Consortium)

The Clinical Data Interchange Standards Consortium (CDISC) standards play a crucial role in clinical research by providing a unified framework for organizing, formatting, and exchanging clinical trial data. By ensuring consistency and interoperability, CDISC standards enhance data quality, facilitate regulatory submissions, and streamline the drug development process. The two most prominent CDISC models are:

Study Data Tabulation Model (SDTM): SDTM defines a standard structure for clinical trial data, ensuring consistent data collection across studies. It organizes data into domains such as Demographics, Adverse Events (AE), and Laboratory Tests.

Example: In a clinical trial for a new medication, patient demographic data, such as age, gender, and race, are captured in the Demographics (DM) domain, while adverse events experienced by participants are recorded in the Adverse Events (AE) domain.

Analysis Data Model (ADaM): ADaM focuses on the data preparation for statistical analysis. It ensures traceability and reproducibility by creating analysis-ready datasets derived from SDTM domains.

Example: Analyzing the efficacy of a drug by comparing pre-treatment and post-treatment lab results, with the data prepared in ADaM format for statistical analysis.


2. OMOP Common Data Model (Observational Medical Outcomes Partnership)

The OMOP Common Data Model is a standardized data model developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative. It enables the transformation of healthcare data from various formats and sources into a common structure, facilitating large-scale observational research and ensuring consistency in data analysis.

OMOP Common Data Model standardizes healthcare data from diverse sources, such as electronic health records (EHRs) and claims databases, to facilitate large-scale observational research. It provides a uniform data structure to enable systematic analyses and cross-database studies.

Example: A study examining the long-term effects of a particular medication on cardiovascular health might use the OMOP model to integrate and analyze data from multiple hospitals’ EHR systems, allowing researchers to identify trends and outcomes more effectively.


3. HL7 FHIR (Fast Healthcare Interoperability Resources)

FHIR is a standard for exchanging healthcare information electronically. It defines how healthcare information can be exchanged between different systems regardless of the technology they use. FHIR uses resources (building blocks) to represent clinical and administrative data.

Example: In a clinical trial, FHIR can be used to exchange patient data between the research database and an EHR system, ensuring that patient records are up-to-date and consistent across platforms.


4. i2b2 (Informatics for Integrating Biology and the Bedside)

i2b2 is a scalable informatics framework designed to enable clinical researchers to use existing clinical data for discovering new insights. It integrates clinical data with genomic data to facilitate translational research.

Example: Using i2b2, researchers can correlate patient genetic information with clinical outcomes to identify potential genetic markers for disease susceptibility or treatment response.


5. Sentinel Common Data Model

The Sentinel Common Data Model, developed by the U.S. Food and Drug Administration (FDA), is used for active safety surveillance of medical products. It standardizes data from various sources, enabling the FDA to monitor the safety of marketed drugs and other medical products in real-time.

Example: Monitoring adverse events associated with a newly approved drug by analyzing healthcare data from insurance claims and EHRs standardized to the Sentinel model.

The selection of a data model in clinical research depends on the specific needs of the study, the type of data being collected, and the desired outcomes. By adopting standardized data models like CDISC, OMOP, FHIR, i2b2, and Sentinel, researchers can ensure data consistency, enhance data sharing, and improve the overall quality and efficiency of clinical research.


Understanding these common data models and their applications helps in designing robust studies, achieving regulatory compliance, and ultimately advancing medical science through better data management and analysis.

In the next posts, I will present individually and in more details each of the models viewed in this article.